Understanding Cache: Concepts, Types, and Performance Optimization in High-Concurrency Scenarios

1. Introduction

Cache technology can greatly reduce computation and improve response speed, but no single solution fits all scenarios; selecting the appropriate cache requires balancing cost, efficiency, and specific business requirements.

2. Key Points

Basic concepts of cache

CPU cache

Distributed cache principles

Factors affecting cache efficiency

Solutions for high‑concurrency cache issues

3. Understanding Cache

3.1 Narrow definition

Cache refers to CPU cache, where data is first looked up in the fast CPU cache before accessing slower memory.

3.2 Broad definition

Any structure that bridges two components with large speed differences to coordinate data transfer can be called a cache.

3.3 Advantages

Cache can be placed at various layers of a web architecture (database, application, web server, client, CPU‑memory, OS disk) to improve performance, stability, and availability.

4. CPU Cache Overview

CPU cache sits between the CPU and main memory, providing fast temporary storage that mitigates the speed gap; typical hierarchy includes L1, L2, L3 caches built with SRAM.

5. Distributed Cache

5.1 Local cache

Examples include Ehcache and Guava Cache; they are fast but not shareable across processes.

5.2 Characteristics

High‑performance reads, dynamic scaling, automatic failover, load balancing; common implementations are Memcached, Redis, and Alibaba Tair.

5.3 Implementation principles

Data reading uses consistent hashing to locate nodes; data is evenly distributed by virtual nodes; hot‑standby copies ensure redundancy.

6. Factors Influencing Cache Performance

6.1 Serialization

In‑process caches avoid serialization, while off‑heap caches require it, adding CPU overhead.

6.2 Hit rate

Higher hit rates improve latency, throughput, and concurrency; hit rate depends on business scenarios, cache granularity, and expiration strategies.

6.3 Cache eviction strategies

When cache is full, algorithms such as FIFO, LFU, LRU, ARC, MRU are used to decide which entries to discard.

7. Common Cache Problems in High‑Concurrency

7.1 Cache penetration

Requests for non‑existent keys repeatedly hit the database; solutions include placeholder keys, short‑TTL empty results, or Bloom filters.

7.2 Cache stampede

Simultaneous cache misses cause many threads to query the DB; a lock around cache miss handling can mitigate this.

7.3 Cache avalanche

Mass expiration at the same time overloads the DB; adding random jitter to TTL spreads expirations.

Conclusion

Understanding cache fundamentals, selecting appropriate types, and applying proper strategies are essential for improving system performance, stability, and availability.